Overlapping generations model

This note presents the simplest overlapping generations model. The model is due
to Diamond (1965), who built on earlier work by Samuelson (1958).
Overlapping generations models capture the fact that individuals do not live
forever, but die at some point and thus have finite life-cycles. Overlapping generations
models are especially useful for analysing the macro-economic effects of
different pension systems.

Chapter 9 Overlapping Generations Models
This chapter describes the pure-exchange overlapping generations model of Paul Samuelson (1958). We begin with an abstract presentation that treats the overlapping generations model as a special case of the chapter 8 general equilibrium model with complete markets

We present a discriminative model that directly predicts which set of phrasal translation rules should be extracted from a sentence pair. Our model scores extraction sets: nested collections of all the overlapping phrase pairs consistent with an underlying word alignment. Extraction set models provide two principle advantages over word-factored alignment models. First, we can incorporate features on phrase pairs, in addition to word links. Second, we can optimize for an extraction-based loss function that relates directly to the end task of generating translations. ...

This book is concerned with the computational processing of 3D faces, with applications in human computer interaction. It is a discriplinary research area overlapping with computer vision, computer graphics, machine learning and HCI. Within the last 10 years, fast increase in performance of memory, display and processor
speed has allowed the expansion of Computer Graphics. It has now overcome Image
Processing in its achievement. In the 3D face field, the CG-generated faces are almost
indiscernible from real faces. Still it requires manual drawing for each image and artistic
skills.

Extractive methods for multi-document summarization are mainly governed by information overlap, coherence, and content constraints. We present an unsupervised probabilistic approach to model the hidden abstract concepts across documents as well as the correlation between these concepts, to generate topically coherent and non-redundant summaries. Based on human evaluations our models generate summaries with higher linguistic quality in terms of coherence, readability, and redundancy compared to benchmark systems. ...